|From||"Roger Brugge" <firstname.lastname@example.org>|
|Date||Wed, 25 Sep 2013 10:16:27 +0000|
Forwarded from CLIMLIST... Vacancy: Research Associate, Cloud Dataset Development and Analysis The Cooperative Institute for Climate and Satellites (CICS) at the University of Maryland seeks to fill a professional scientific position for its collaborative research as a Cooperative Institute with the National Oceanic and Atmospheric Administration (NOAA). This position will be associated with the Air Resources Laboratory in NOAA Research. The position will be located at the NOAA Climate & Weather Prediction Center in College Park, Maryland. The position is funded for one year only. Background This ongoing project involves the development and validation of a homogeneity-adjusted dataset of cloud cover from weather observations for the past ~50 years, comparison of this dataset to other sources of cloud data and other relevant climate variables, and assessment of variability, trends, and uncertainties in cloud data, with emphasis on data from the U.S. The goal of the project is to improve our understanding of past changes in cloud cover on multi-decadal scales. Responsibilities The individual will contribute to the goals of the project, including: 1. Data processing and analysis of ground-based cloud data, satellite cloud data, and/or related climate variables, and/or analysis of climate model data for comparison to the cloud data, in order to clarify long-term variability and trends in cloud cover. 2. Extension of existing station data to create a more spatially complete dataset. 3. Publication of results in peer-reviewed literature and presentation at scientific conferences. Qualifications Requirements for this position include a M.S. (PhD preferred) in a field related to climate studies, proficiency with a programming language (FORTRAN is preferred), and with scientific graphics software, and demonstrated ability to work independently and analyze scientific data with minimal supervision. Knowledge of and experience with issues relating to extracting long-term climate signals from weather observations or satellite records will be considered a plus. Familiarity with methods to extend sparse data to create a spatially and temporally more complete dataset is also a plus. The candidate must be able to work well with others and to communicate effectively both orally and in writing. To Apply: Interested candidates should send a CV with a list of at least 3 professional references and a cover letter explaining how your qualifications meet the posted requirements to email@example.com <mailto:firstname.lastname@example.org>.
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